Application of AI for Incipient Fault Diagnosis and Condition Assessment of Power Transformer

Application of AI for Incipient Fault Diagnosis and Condition

Assessment of Power Transformer

Under the guidance of

Dr. R.K. JarialBy:

Hasmat Malik

M. Tech, 2nd Year

Roll No. 10M26210/08/2012

2

Contents1.Introduction 2.Literature Survey3.Methodology for Power Transformer Incipient Fault Diagnosis (TIFD) and Condition Assessment 4.Application of AI for Power Transformer Incipient Fault Diagnosis and Condition Assessment Using MATLAB5.Result Comparison Between Conventional and AI Methods 6.Future work7.Conclusion8.List of Publications9.References

Condition Failures With

OLTC

Failures Without OLTC

Tank 6% 17.4%

Tap Changer

40% 4.6%

Winding + Core

35% 33%

Auxiliaries 5% 11%

Bushing + Terminals

14% 33.3%

CIGRE Bath-tub Failure Curve for Power Transformers [19]

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1. Introduction Power Transformer and its Failure[19]

3

Faults and Its Related Gases

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Conti.

4IEEE C57.104-2008

5

Importance Level of Various Transformer Diagnostic MethodsConti.

Importance level of various diagnostic methods

Diagnostic Methods for transformer are:Chemical Diagnostic MethodsElectrical Diagnostic methodsOptical/Thermal Diagnostic MethodsMechanical Diagnostics Methods

Electrical analysis such as:•Partial discharge (PD) analysis•DBV measurement•DDF or tan delta measurement•FRA measurement

Chemical analysis such as:•Moisture analysis •Dissolved gas analysis (DGA)•DP measurement•Furan analysis by HPLC.

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Technique Area Number of

Application

Conventional

Techniques

Transformer fault diagnosis and condition assessment 323

AI Techniques Fault diagnosis of power transformers

Condition Monitoring of Power Transformers

Transformer Insulation Diagnosis

On-line detection of dissolved gases in oil

Transformer health condition estimation

Intelligent decision support for diagnosis of incipient faults

23

Neural Networks

(ANNs)

Fuzzy-Logic (FL) Transformer fault diagnosis and condition assessment 17

Support vector Machine

(SVM)

Transformer fault/insulation diagnosis 7

Neuro-Fuzzy (ANFIS) A hybrid tool for detection of incipient faults 16

SVM with PSO Model for gas/liquid two-phase flow

Dissolved gas in oil forecasting model

5

ANN with Expert system Automated on-line monitoring and fault diagnosis system 7

2. Literature Survey

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NN with SVM Model for condition monitoring of transformer 3

Wavelet network Efficient fault diagnosis system 1

Wavelet with NN Transformer fault diagnosis 1

GA Incipient fault prediction 2

GA with wavelet A data mining approach to dissolved gas analysis 3

GP with Bootstrap Feature Extraction of dissolved gases in oil 1

SVM with Bootstrap Dissolved Gas Analysis 1

SVM with rough set Transformer Fault Diagnosis 1

Evolving wavelet Model for power transformer condition monitoring 1

CMAC Incipient fault analysis 1

PSO Fault classification 1

AIN Fault Diagnosis 1

ANN with PSO Fault Diagnosis 1

FFT with ANN Power Transformer Fault Diagnosis 1

Conti.

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3. Methodology for Transformer fault Diagnosis and Condition Assessment

Using DGA

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1. IEEE Methods Doernendurg Ratio Method Roger’s Ratio Method Key gas Method TDCG Method Insulation Condition Estimation Method2. IEC Methods IEC Ratio Method Duval Triangle's Method

Various Conventional Methods for Transformer Incipient Fault Diagnosis

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3. Other Methods Denkyoken Method CIGRE’s Method Nomograph Method NBR-7274 Method IS-10593:2006 Method Xiaohui Li Method

Conti.

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A. Transformer Fault Diagnosis Using IEC Method

IEC 60599

No. Fault Type C2H2/C2H4 CH4/H2 C2H4/C2H6

0 No fault 0 0 01 Partial discharges of low

energy density0 1 0

2 Partial discharges of high energy density

1 1 0

3 Discharge of low energy 1 or 2 0 1 or 24 Discharge of high energy 1 0 25 Thermal fault of low

temperature, <150 oC0 0 1

6 Thermal fault of low temperature, 150-300 oC

0 2 0

7 Thermal fault of medium temperature, 300-700 oC

0 2 1

8 Thermal fault of high temperature, >700 oC

0 2 2

Ratio Codes 0 1 2

C2H2/C2H4 <0.1 0.1-3 >3

CH4/H2 0.1-1 <0.1 >1

C2H4/C2H6 <1 1-3 >3

IEC Ratio Codes Classification of Faults according to the IEC gas ratio codes

Conti.

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B. Transformer Paper Insulation Deterioration Condition Estimation

Paper Status Condition Using CO2 and CO

CO2 CO Condition

X1=0-2500 Y1=0-350 Normal Operation (NO)

X2=2500-4000 Y2=351-570 Modest Concern (MCI)

X3=4001-10,000 Y3=571-1400 Major Concern (MCMI)

X4=>10,000 Y4=>1400 Imminent Risk (IRF)

As per IEEE Standard C57.104™ Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers [4] gives status conditions based on accumulated values of CO2 and CO.

Accumulated dissolved gas levels provide four status conditions :

Conti.

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C. Transformer Diagnosis Using TDCG

IEEE C57.104-2008

Status/TDCG TDCG_Rates(ppm/day)

Sampling Intervals and Operating Procedures for Gas Generation Rates

Sampling Interval (SI)

Operating Procedure (OP)

Condition 1<720

<10 6 Monthly (SIA) Continue normal operation (OPA)

10-30 Quarterly (SIQ)

>30 Monthly (SIM) Exercise caution. Analise individual gases to find cause. Determine load dependant (OPB)

Condition 2721-1920

<10 Quarterly (SIQ) Exercise caution. Analise individual gases to find cause. Determine load dependant (OPB)

10-30 Monthly (SIM)

>30

Condition 31921-4630

<10 Monthly (SIM) Exercise caution.Analise individual gases to find cause. Plan outage. (OPC)

10-30 Weekly (SIW)

>30

Condition 4>4630

<10 Weekly (SIW) Exercise caution. Analise individual gases to find cause. Plan outage. (OPC)10-30 Daily (SID)

>30 Consider removal from service

TDCG = C2H2+C2H4+ H2+CH4

+C2H6 + CO

TDCG_Rate = (St-So)/T Where St = ppm value of Current TDCG; So = ppm value of Previous TDCG T = Time in days.

Conti.

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4. Application of AI in Power Transformer Fault Diagnosis and Condition

Assessment

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Various Techniques for Transformer Fault Diagnosis and Condition Assessment Fuzzy-Logic Artificial Neural Network Support Vector Machine (SVM) Swarm Intelligence Technique (SIT) Learning Vector Quantization (LVQ) Genetic Algorithm (GA) Genetic Programming (GP) Probabilistic Neural Network (PNN)

etc.

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The Design Methodology of ANN Fault Diagnostic System

Neural Network System

Planning

NW Assigning

Assign NW Performance

Collect the DataCollect the Data

Create the NetworkCreate the Network

Configure the NetworkConfigure the Network

Initialize the Weights and Biases

Initialize the Weights and Biases

Train the NetworkTrain the Network

Validate the NetworkValidate the Network

Use the NetworkUse the Network

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17

function DGA_Analysis% Here Input and Output variables are defined as: H - input data. T - target data.inputs = H'; targets = T'; % Then we Create an Neural NetworkhiddenLayerSize = 10; net = fitnet(hiddenLayerSize); % After creat Network; Make Division of Data for %Training, Validation, Testingnet.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; % then Train the Network[net,tr] = train(net,inputs,targets);

% After satisfactory training; Test the Networkoutputs = net(inputs);errors = gsubtract(targets,outputs);performance = perform(net,targets,outputs)

% View the Networkview(net)end

Click prescribed button to view the graph:

A. Transformer Fault Diagnosis Using ANN

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Now click

Neural Network Performance Graph

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ANN Based Result Comparison With Conventional Method

S.NFault Estimation as per IEC Method Fault Estimation as per ANN Method

IEC code Type of Fault ANN fault components

Type of Fault

1020

Thermal fault of low temperature (150-300oC)

F(6)=5.99998 Thermal fault (150-300oC)

2022

Thermal fault of high temperature (>700oC)

F(8)=8.00000 Thermal fault (>700oC)

3021

Thermal fault of medium temperature (300-700oC)


4120

No match F(N)=4.99995 Thermal fault (<150oC)

5101

Discharge of low energy F(3)=3.00000 Discharge of low energy

6212


7000

No fault F(0)=0.00001 No fault

8001

Thermal fault of low temperature (<150oC)

F(5)=4.99999 Thermal fault (<150oC)

9102

Discharge of high energy F(4)=4.00000 Discharge of high energy

10010

Partial discharge of low energy density

F(1)=0.99999 PD of low energy

Conti.

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B. Transformer Fault Diagnosis Using Probabilistic Neural Network-PNN function pnn_fault_Analysis% Here Input and Target variables are defined as:P = [R1 R2 R3; R1 R2 R3;...... ]';Tc = [class of fault]; % To plotplot(P(1,:),P(2,:),'.','markersize',20)for i=1:145, text(P(1,i)+0.1,P(2,i),sprintf('condition %g',Tc(i))), endaxis([0 10 0 10])title('Three vectors and their classes.')xlabel('P(1,:)')ylabel('P(2,:)') % we convert the target class indices Tc to vectors T.% Then we design a probabilistic neural network with NEWPNN.T = ind2vec(Tc);spread = 1;net = newpnn(P,T,spread);

% Manage the size of windowA = net(P);Ac = vec2ind(A);plot(P(1,:),P(2,:),'.','markersize',10)axis([0 10 0 10])for i=1:7,text(P(1,i)+0.1,P(2,i),sprintf(' %g',Ac(i))),endtitle('Testing the network.')xlabel('P(1,:)')ylabel('P(2,:)')

20

% Now test Network on the design input vectors.p = [R1 R2 R3; R1 R2 R3;......];a = net(p);ac = vec2ind(a);hold onplot(p(1),p(2),'.','markersize',30,'color',[1 0 0]) text(p(1)+0.1,p(2),sprintf('class %g',ac))hold offtitle('Classifying a new vector.')xlabel('P(1,:) and p(1)')ylabel('P(2,:) and p(2)')end

Conti.

PNN Based Fault Classification

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PNN Based Result Comparison With Conventional Method

S.NFault Estimation as per IEC Method Fault Classification as per PNN

Method

IEC code Type of Fault PNN fault classification

Type of Fault

1020

(6)Thermal fault of low temperature (150-300oC)

Fault 6 Thermal fault (150-300oC)

2022

(8)Thermal fault of high temperature (>700oC)

Fault 8 Thermal fault (>700oC)

3021

(7)Thermal fault of medium temperature (300-700oC)


4120

No match Fault 5 Thermal fault (<150oC)

5101

(3)Discharge of low energy Fault 3 Discharge of low energy

6212


7000

(0)No fault Fault 0 No fault

8001

(5)Thermal fault of low temperature (<150oC)

Fault 5 Thermal fault (<150oC)

9102

(4)Discharge of high energy Fault 4 Discharge of high energy

10010

(1)Partial discharge of low energy density

Fault 1 PD of low energy

Conti.

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C. Transformer Fault Diagnosis Using Learning Vector Quantization-LVQFunction LVQ_fault_Analysistic% Here Input and Target variables are defined as:P = [R1 R2 R3 R4, R5; R1 R2 R3 R4 R5;...... ]';Tc = [class of fault];

% we convert the target class indices Tc to vectors T.T = ind2vec(Tc); % Then we design a Learning Vector Quantization with newlvq.% To plot, plotvec(P,C)title('Input Vectors');xlabel('P(1)');ylabel('P(2)');net = newlvq(minmax(P),4,[.6 .4],0.1);hold onW1 = net.IW{1};plot(W1(1,1),W1(1,2),'ow')title('Input/Weight Vectors LQV');xlabel('P(1), W(1)');ylabel('P(2), W(3)'); %To train the network first override the default number of epochs,and then train the networknet.trainParam.epochs=150;net.trainParam.show=Inf;net=train(net,P,T);

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%When training is finished, replot the input vectors '+' and the competitive neurons' weight vectors 'o'.%-------------------------------------------------------------------------------------------cla;% plot(P(1,:),P(2,:),'.','markersize',10)plotvec(P,C);hold on;plotvec(net.IW{1}',vec2ind(net.LW{2}),'o');axis([0 20 0 20])%--------------------------------------------------------------------------------------------%Now use the LVQ network as a classifier, %enter value for fault classification%--------------------------------------------------------------------------------------------p = [R1 R2 R3 R4 R5];fault_type = vec2ind(net(p))ac = vec2ind(fault_type);hold onplot(p(1),p(2),'.','markersize',30,'color',[0 0 1]) %for colur change of dot text(p(1)+0.1,p(2),sprintf('class %g',ac))hold offtitle('LVQ Baesd Incipient Fault Classification.')xlabel('P(1,:) and p(1)')ylabel('P(2,:) and p(2)')Tocend

Conti.

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Now click

LVQ Performance Graph

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LVQ Based Result Comparison With Conventional Method

S.NFault Estimation as per IEC Method Fault Classification as per LVQ

Method

IEC code Type of Fault LVQ fault classification

Type of Fault

1020



2022


Fault 8 Thermal fault (>700oC)

3021



4120


5101

(3)Discharge of low energy Fault 3 Discharge of low energy

6212


7000

(0)No fault Fault 0 No fault

8001


Fault 5 Thermal fault (<150oC)

9102

(4)Discharge of high energy Fault 4 Discharge of high energy

10010


Fault 1 PD of low energy

Conti.

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D. Design Methodology for Fuzzy Fault Diagnosis System

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28

Transformer Fault Diagnosis Using Fuzzy-Logic

General scheme of FLTFD

Conti.

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FLM Based Result Comparison With Conventional Method

S.NFault Estimation as per IEC Method Fault Estimation as per Fuzzy Logic

Method

IEC code Type of Fault Fuzzy fault components

Type of Fault

1020



2022



3021



4120


5101


6212


7000


8001



9102


10010

Peartial discharge of low energy density


Conti.

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E. Design Methodology for SVM Fault Diagnosis System

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Transformer Fault Diagnosis Using SVM

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SVM Based Result Comparison With Conventional Method

S.NFault Estimation as per IEC Method Fault Estimation as per SVM Method

IEC code Type of Fault SVM fault components

Type of Fault

1020



2022



3021



4120


5101


6212

No match F(N)=5.899=6 Thermal fault (<150oC)

7000


8001



9102


10010

Peartial discharge of low energy density


Conti.

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Result Comparison Between AI Methods and Conventional Method

S.N

Fault Estimation as per IEC Method Fault Estimation as per AI Method

IEC code

Type of Fault Fuzzy fault components

ANN fault components

PNN fault classification

LVQ fault components

SVM fault components

1020


F(6)=5.98 F(6)=5.99998 Fault 6 Thermal fault (150-300oC)

Thermal fault (150-300oC)

2022


F(8)=8.0 F(8)=8.00000 Fault 8 Thermal fault (>700oC)

Thermal fault (>700oC)

3021


F(7)=6.99 F(7)=6.99999 Fault 7 Thermal fault (300-700oC)


4120

No match F(N)=5.0 F(N)=4.99995 Fault 5 Thermal fault (<150oC)

Thermal fault (<150oC)

5101

(3)Discharge of low energy F(3)=3.0 F(3)=3.00000 Fault 3 Discharge of low energy

Discharge of low energy

6212

No match F(N)=5.0 F(N)=4.99995 Fault 5 Thermal fault (<150oC)


7000

(0)No fault F(0)=0.387 F(0)=0.00001 Fault 0 No fault No fault

8001


F(5)=4.99 F(5)=4.99999 Fault 5 Thermal fault (<150oC)

Thermal fault (<150oC)

Conti.

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9102

(4)Discharge of high energy F(4)=4.0 F(4)=4.00000 Fault 4 Discharge of high energy

Discharge of high energy

10010


F(1)=0.99 F(1)=0.99999 Fault 1 PD of low energy

PD of low energy

Conti.

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Future WorkDevelopment of a Software for Intelligent Diagnostics of Power Transformers, using the technique of ANN, fuzzy logic and SVM.Database system management software will be developed by using Visual Basic 6.0 and Microsoft SQL Server 2000. Bio-sensors based hardware system can be designed and implemented for more quicker and reliable transformer fault diagnosis technique by interfacing with communication network for rapid action using SCADA system. SCADA system can also provide wireless communication interfacing in between sensors based hardware and software for transformer fault diagnosis Co-relation between all purposed method for Transformer condition assessment and other new AI based techniques

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Conclusion On-line monitoring and diagnostics is a useful tool to help operators to manage their assets and make decisions on continuing operation, maintenance or replacement.

Dissolved Gas Analysis (DGA) is the heart of on-line monitoring and can give warning of 70% of the most common failures in power transformers.

The monitoring systems can be used to carry out in depth analysis of the condition of the transformer insulation and accessories.

Due to the reason of not matching conditions (i.e. codes), several transformers could not be diagnosed by using the Conventional method but are diagnosed by the fuzzy- Logic, ANN, LVQ, PNN and SVM methods.

Analysis and Calculations are become easy.

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List of Publications[1] Hasmat Malik, Tarkeshwar and R.K. Jarial, "Make Use of DGA to Carry Out the Transformer

Oil-Immersed Paper Deterioration Condition Estimation with Fuzzy-Logic", in Elsevier/ScienceDirect Procedia Engineering, ISSN: 1877-7058, Vol. 30, pp. 587-595, Dec. 2011.

[2] Hasmat Malik, R.K. Jarial and Abdul Azeem, “Application Research Based on Modern Technology for Transformer Health Index Estimation”, in Proc. IEEE Int. Multi Conf. on Systems, Signals and Devices (SSD), Pp. 1-7, 20-23 March 2012, Chemnitz, Germany.

[3] Hasmat Malik, Tarkeshwar and R.K. Jarial, “An Expert System for Incipient Fault Diagnosis and Condition Assessment in Transformers”, in Proc. IEEE Int. Conf. on Computational Intelligence and Communication Networks, pp. 138-142, 2011.

[4] Hasmat Malik, Amit Kr.Yadav, Tarkeshwar and R.K. Jarial, “Make Use of UV/VIS Spectrophotometer to Determination of Dissolved Decay Products in Mineral Insulating Oils for Transformer Remnant life Estimation with ANN”, in Proc. IEEE Int. Conf. on Engineering Sustainable Solutions (INDICON-2011), Pp.1-6.

[5] Hasmat Malik, R.K. Jarial, B.Anil Kr and Mantosh Kr., “Application Research Based on Modern Technology to Investigating Causes and Detection of Failures in Transformers on the Bases of Importance Level” in Proc. IEEE Int. Conf. on Engineering Sustainable Solutions (INDICON-2011), Pp.1-6.

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[6] Hasmat Malik, R.K. Jarial, Tarkeshwar, and B.Anil Kr, “Fuzzy-Logic Applications in Transformer Diagnosis Using Individual and Total Dissolved Key Gas Concentrations” has been accepted for publication in free International Journal of Electrical Engineering (IJEE) ISSN 1582-4594.

[7] Hasmat Malik, Shafqak Mugal, YR Sood and R.K. Jarial, “Application and Implementation of Artificial Intelligence in Electrical System” in Proc. IEEE Spencer Int. conf. on Advances in Computing & Communication (ICACC-2011) ISBN 978-81-920874-0-5, Sponsored by IEEE-MTTS, pp. 499-505.

[8] Hasmat Malik, R.K. Jarial, “UV/VIS Response Based Fuzzy-logic for Health Assessment of Transformer Oil” in Elsevier/ScienceDirect Procedia Engineering, ISSN: 1877-7058, Vol. 30, pp. 932-939, Dec. 2011.

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1. IEEE C57.104-2009 “IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers"2. IEC 60599-2007/05 “Guide to the interpretation of dissolved and free gases analysis of Mineral oil-impregnated electrical equipment in

service.”3. A. International, "Standard Test Method for Analysis of Gases Dissolved in Electrical Insulating Oil by Gas Chromatography," ASTM D

3612, 2002.4. Facilities Instructions, Standards, and Techniques (FIST) Volume 3-31 2003; ”Transformer Diagnostics”5. N. Yadaiah and Nagireddy Ravi, “Fault Detection Techniques for Power Transformers,” Industrial & Commercial Power Systems

Technical Conference, 2007. ICPS 2007. IEEE/IAS Vol.6 , Issue ,11 May 2007, pp:1–9.6. M. Duval, “A review of fault detectable by gas-in-oil analysis in transformers,” IEEE electrical Insulation Magazine, Vol. 10, No. 3, pp

8-17, 2002.7. I. E. Commission, "Insulating Liquids-Oil Impregnated Paper and Pressboard- Determination of Water by Automatic Coulometric Karl

Fischer Titration,," Intern. Electrotechnical Commission (IEC), 1997.8. Jashandeep Singh, Yog Raj Sood, and Raj Kumar Jarial, and Piush Verma, “Condition Monitoring of Power Transformers—

Bibliography Survey” IEEE Elect. Insul. Mag., vol. 24, no. 3, pp. 11–25, May/June 2008.9. T. K. Saha, "Review of modern diagnostic techniques for assessing insulation condition in aged transformers ," Dielectrics and

Electrical Insulation, IEEE Transactions on [see also Electrical Insulation, IEEE Transactions on], vol. 10, pp. 903-917, 2003.10. Lapworth John and McGrail Tony, “Transformer Failure Modes and Planned Replacement”, IEE Colloquium on Transformer life

management (Ref. No. 1998/510), 22 Oct. 1998, pp:9/1 - 9/711. M.Warn and A. J. Vandermaar “Review of condition assessment of power transformers in service”, IEEE Electrical insulation

Magazine, vol. 18, no. 6, pp. 12-25, NoviDecZG92.12. K. F. Thang, R. K. Aggarwal, A. J. MacGrail and D. G. Esp, “Application of Self-Organizing Map Algorithms for Analysis and

Interpretation of Dissolved Gases in Power Transformers,” Power Engineering Society Summer Meeting, 2001. IEEE, Vancouver, BC, Canada,07 /15/2001 Vol. 3,pp 1881-1886.

13. K. F. Thang, R. K. Aggarwal, D. G. Esp, and A. J. MacGrail, “Statistical and Neural Analysis of Dissolved Gases in Power Transformers” ,Eighth International Conference on Dielectric Materials, Measurements and Applications, 2000. (IEE Conf. Publ. No.

473) 09/17-21/2000, Edinburgh, UK, pp 324-329.

References

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14 Deepika Bhalla, RK Bansal, HO Gupta, “Application of Artificial Intelligence Techniques for Dissolved Gas Analysis of Transformers-A Review”, world Academy of Science, Engineering and Technology 62, pp. 221-229.15 V.Durainsamy, N.Deverajan: “Neuro-fuzzy schemes for fault detection in power transformer” ScienceDirect Journal of Applied Soft computing ISSN 1568-4946, 2007, pp. 534-539. 16 Xiaohui Li, H Wu.: “DGA Interpretation Scheme Derived From Case Study”, IEEE Transactions on Power Delivery, Vol.26, No.2, April 2011, pp. 1292-1293.17 ASTM, "Standard Test Method for Dielectric Breakdown Voltage of Insulating Oils of Petroleum Origin Using VDE Electrodes," ASTM D 1816, 2004.18 ASTM, "Standard Test Method for Dielectric Breakdown Voltage of Insulating Liquids Using Disk Electrodes," ASTM D 877, 2002.19 A.A. International, "Standard Test Method for Measurement of Average Viscometric Degree of Polymerization of New and Aged Electrical Papers and Boards," ASTM D 4243, 1999.04.10 1999.20 ASTM, "Standard Test Method for Furanic Compounds in Electrical Insulating Liquids by High-Performance Liquid Chromatography (HPLC)," ASTM D5837 -05, 2005.21 Fabio R. Barbora, OM Almeida, “Artificial Neural Network Application In Estimation of Dissolved Gases In Insulating Mineral

Oil Physical-Chemical Datas for Incipient Fault Diagnosis”, in Proc. IEEE Conf. pp.706-710, 2011.22 Li Song, Li Xiu and WW, “Fault Diagnosis of transformer Based on Probabilistic Neural Network”, in Proc. IEEE Int. Conf. on

intelligent computation technology and automation, pp.128-131, 2011. 23 Tsair-Fwu Lee,Ming-yuan, “Partical Swarm Optimization-Based SVM Application: Power Transformers Incipient Fault

Syndrome Diagnosis”, in Proc. IEEE Int Conf. on Hybrid Information Technology, pp. 319-227, 2010.24 Hasmat Malik, R.K Jarial, “An Expert System for Power Transformers Incipient Fault Diagnosis and Condition Assessment” in Proc.

IEEE International Conference n Computational Intelligence and Communication Systems, pp. 138-142, Oct.2011.25 W.H. Tang and Q.H. Wu , “Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence”

Book, Springer Publisher- Verlag London Limited 2011, ISSN 1612-1287,and ISBN 978-0-85729-051-9, 2011.

Conti.

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THANK YOU

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Application of AI for Incipient Fault Diagnosis and Condition Assessment of Power Transformer

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